Instructions to use razor5050/tinyllm-LoRA-Finetuned-25million with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use razor5050/tinyllm-LoRA-Finetuned-25million with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="razor5050/tinyllm-LoRA-Finetuned-25million")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("razor5050/tinyllm-LoRA-Finetuned-25million")
model = AutoModelForCausalLM.from_pretrained("razor5050/tinyllm-LoRA-Finetuned-25million")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use razor5050/tinyllm-LoRA-Finetuned-25million with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "razor5050/tinyllm-LoRA-Finetuned-25million"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razor5050/tinyllm-LoRA-Finetuned-25million",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/razor5050/tinyllm-LoRA-Finetuned-25million

SGLang

How to use razor5050/tinyllm-LoRA-Finetuned-25million with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "razor5050/tinyllm-LoRA-Finetuned-25million" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razor5050/tinyllm-LoRA-Finetuned-25million",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "razor5050/tinyllm-LoRA-Finetuned-25million" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razor5050/tinyllm-LoRA-Finetuned-25million",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use razor5050/tinyllm-LoRA-Finetuned-25million with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for razor5050/tinyllm-LoRA-Finetuned-25million to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for razor5050/tinyllm-LoRA-Finetuned-25million to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for razor5050/tinyllm-LoRA-Finetuned-25million to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="razor5050/tinyllm-LoRA-Finetuned-25million",
    max_seq_length=2048,
)

Docker Model Runner
How to use razor5050/tinyllm-LoRA-Finetuned-25million with Docker Model Runner:
```
docker model run hf.co/razor5050/tinyllm-LoRA-Finetuned-25million
```

Tiny-LLM 25M NQ SFT

This repository contains a 25M-parameter decoder-only language model fine-tuned with supervised fine-tuning (SFT) on the sentence-transformers/natural-questions dataset.

Model Summary

Base model: custom Tiny-LLM 25M decoder-only model
Export format: Hugging Face Transformers-compatible causal language model
Fine-tuning method: Unsloth LoRA / QLoRA, merged into a standalone model
Training dataset: sentence-transformers/natural-questions
Task: lightweight question-answering style text generation

Prompt Format

User: {question}
Assistant:

The model was fine-tuned to continue from that prompt into an answer.

Training Details

Dataset

Source: sentence-transformers/natural-questions
Total usable records: 100,231
Training examples: 98,227
Validation examples: 2,004
Split seed: 1337
Answer handling: verbatim answers with light normalization

Fine-Tuning Setup

Framework: Unsloth
Max sequence length: 512
Epochs: 2
Effective batch size: 32
Final artifact: merged Hugging Face model

Intended Use

This model is intended for:

lightweight QA experiments
Tiny-LLM fine-tuning experiments
small-scale text generation testing
research and prototyping

Limitations

This is a very small 25M model and may produce inaccurate, incomplete, or hallucinated answers.
The model was fine-tuned on QA-style prompt-completion data and may not generalize well outside that format.
The model is not suitable for high-stakes, safety-critical, legal, financial, or medical use.

Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "YOUR_USERNAME/tiny-llm-25m-nq-sft"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "User: what is gravity?\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=120,
    temperature=0.2,
    top_p=0.9,
    do_sample=True,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

25M params

Tensor type

F32

razor5050
/

tinyllm-LoRA-Finetuned-25million